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Machine Learning Blog | ML@CMU | Carnegie Mellon University
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Forking-Sequences — Part II: Multi-Horizon Forecast Ensembling with Reduced Volatility
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5 weeks ago
8 min read
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Healthcare Benchmarks Are Only as Good as Their Assumptions
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13w
13 weeks ago
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Pre-Training Isn’t Bitter Enough
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13 weeks ago
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Teaching Vision-Language Models to Speak Cinema
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18w
18 weeks ago
10 min read
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Introducing ARFBench: A time series question-answering benchmark based on real incidents
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20w
20 weeks ago
8 min read
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Carnegie Mellon at ICLR 2026
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21w
21 weeks ago
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When Should AI Step Aside?: Teaching Agents When Humans Want to Intervene
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22w
22 weeks ago
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6 min read
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LumberChunker: Long-Form Narrative Document Segmentation
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26w
26 weeks ago
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Carnegie Mellon at NeurIPS 2025
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31w
31 weeks ago
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Yes, AI, There is a Santa Claus
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38w
38 weeks ago
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Validating LLM-as-a-Judge Systems under Rating Indeterminacy
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40w
40 weeks ago
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Actions for Validating LLM-as-a-Judge Systems under Rating Indeterminacy
Carnegie Mellon at NeurIPS 2025
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41w
41 weeks ago
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How to Explore to Scale RL Training of LLMs on Hard Problems? – Machine Learning Blog | ML@CMU | Carnegie Mellon University
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42w
42 weeks ago
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Actions for How to Explore to Scale RL Training of LLMs on Hard Problems? – Machine Learning Blog | ML@CMU | Carnegie Mellon University
How to Explore to Scale RL Training of LLMs on Hard Problems?
blog.ml.cmu.edu
42w
42 weeks ago
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Actions for How to Explore to Scale RL Training of LLMs on Hard Problems?
Carnegie Mellon University at EMNLP 2025
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45w
45 weeks ago
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Learning from Failure to Tackle Extremely Hard Problems
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46w
46 weeks ago
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Diffusion Beats Autoregressive in Data-Constrained Settings
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51w
51 weeks ago
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Verlog: A Multi-turn RL framework for LLM agents
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52w
52 weeks ago
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Actions for Verlog: A Multi-turn RL framework for LLM agents
Carnegie Mellon University at ICML 2025
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62w
62 weeks ago
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RLHF 101: A Technical Tutorial on Reinforcement Learning from Human Feedback
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67w
67 weeks ago
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